Overview

Dataset statistics

Number of variables19
Number of observations420768
Missing cells3724
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory61.0 MiB
Average record size in memory152.0 B

Variable types

Numeric16
Categorical3

Warnings

df_index is highly correlated with No and 1 other fieldsHigh correlation
No is highly correlated with df_index and 1 other fieldsHigh correlation
year is highly correlated with df_index and 1 other fieldsHigh correlation
RAIN is highly skewed (γ1 = 30.04363268) Skewed
df_index is uniformly distributed Uniform
No is uniformly distributed Uniform
station is uniformly distributed Uniform
hour has 17532 (4.2%) zeros Zeros
RAIN has 403858 (96.0%) zeros Zeros
WSPM has 11118 (2.6%) zeros Zeros

Reproduction

Analysis started2021-01-09 14:36:55.250010
Analysis finished2021-01-09 14:38:28.424790
Duration1 minute and 33.17 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM

Distinct35064
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17531.5
Minimum0
Maximum35063
Zeros12
Zeros (%)< 0.1%
Memory size3.2 MiB
2021-01-09T20:38:28.524824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1753
Q18765.75
median17531.5
Q326297.25
95-th percentile33310
Maximum35063
Range35063
Interquartile range (IQR)17531.5

Descriptive statistics

Standard deviation10122.11694
Coefficient of variation (CV)0.5773674211
Kurtosis-1.200000002
Mean17531.5
Median Absolute Deviation (MAD)8766
Skewness0
Sum7376694192
Variance102457251.4
MonotocityNot monotonic
2021-01-09T20:38:28.629823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
012
 
< 0.1%
1782612
 
< 0.1%
1553312
 
< 0.1%
939012
 
< 0.1%
1143912
 
< 0.1%
3397812
 
< 0.1%
2192012
 
< 0.1%
2396912
 
< 0.1%
1987512
 
< 0.1%
401112
 
< 0.1%
Other values (35054)420648
> 99.9%
ValueCountFrequency (%)
012
< 0.1%
112
< 0.1%
212
< 0.1%
312
< 0.1%
412
< 0.1%
ValueCountFrequency (%)
3506312
< 0.1%
3506212
< 0.1%
3506112
< 0.1%
3506012
< 0.1%
3505912
< 0.1%

No
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM

Distinct35064
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17532.5
Minimum1
Maximum35064
Zeros0
Zeros (%)0.0%
Memory size3.2 MiB
2021-01-09T20:38:28.745825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1754
Q18766.75
median17532.5
Q326298.25
95-th percentile33311
Maximum35064
Range35063
Interquartile range (IQR)17531.5

Descriptive statistics

Standard deviation10122.11694
Coefficient of variation (CV)0.5773344899
Kurtosis-1.200000002
Mean17532.5
Median Absolute Deviation (MAD)8766
Skewness0
Sum7377114960
Variance102457251.4
MonotocityNot monotonic
2021-01-09T20:38:28.844855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
204912
 
< 0.1%
1987512
 
< 0.1%
939012
 
< 0.1%
1143912
 
< 0.1%
3397812
 
< 0.1%
2192012
 
< 0.1%
2396912
 
< 0.1%
1782612
 
< 0.1%
3011612
 
< 0.1%
1425212
 
< 0.1%
Other values (35054)420648
> 99.9%
ValueCountFrequency (%)
112
< 0.1%
212
< 0.1%
312
< 0.1%
412
< 0.1%
512
< 0.1%
ValueCountFrequency (%)
3506412
< 0.1%
3506312
< 0.1%
3506212
< 0.1%
3506112
< 0.1%
3506012
< 0.1%

year
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
2016
105408 
2014
105120 
2015
105120 
2013
88128 
2017
16992 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1683072
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013
2nd row2013
3rd row2013
4th row2013
5th row2013
ValueCountFrequency (%)
2016105408
25.1%
2014105120
25.0%
2015105120
25.0%
201388128
20.9%
201716992
 
4.0%
2021-01-09T20:38:29.023427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-01-09T20:38:29.088254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2016105408
25.1%
2014105120
25.0%
2015105120
25.0%
201388128
20.9%
201716992
 
4.0%

Most occurring characters

ValueCountFrequency (%)
2420768
25.0%
0420768
25.0%
1420768
25.0%
6105408
 
6.3%
4105120
 
6.2%
5105120
 
6.2%
388128
 
5.2%
716992
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1683072
100.0%

Most frequent character per category

ValueCountFrequency (%)
2420768
25.0%
0420768
25.0%
1420768
25.0%
6105408
 
6.3%
4105120
 
6.2%
5105120
 
6.2%
388128
 
5.2%
716992
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common1683072
100.0%

Most frequent character per script

ValueCountFrequency (%)
2420768
25.0%
0420768
25.0%
1420768
25.0%
6105408
 
6.3%
4105120
 
6.2%
5105120
 
6.2%
388128
 
5.2%
716992
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1683072
100.0%

Most frequent character per block

ValueCountFrequency (%)
2420768
25.0%
0420768
25.0%
1420768
25.0%
6105408
 
6.3%
4105120
 
6.2%
5105120
 
6.2%
388128
 
5.2%
716992
 
1.0%

month
Real number (ℝ≥0)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5229295
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size3.2 MiB
2021-01-09T20:38:29.147945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.44870728
Coefficient of variation (CV)0.5287052818
Kurtosis-1.208056663
Mean6.5229295
Median Absolute Deviation (MAD)3
Skewness-0.009293857222
Sum2744640
Variance11.8935819
MonotocityNot monotonic
2021-01-09T20:38:29.219478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
135712
8.5%
335712
8.5%
535712
8.5%
735712
8.5%
835712
8.5%
1035712
8.5%
1235712
8.5%
434560
8.2%
634560
8.2%
934560
8.2%
Other values (2)67104
15.9%
ValueCountFrequency (%)
135712
8.5%
232544
7.7%
335712
8.5%
434560
8.2%
535712
8.5%
ValueCountFrequency (%)
1235712
8.5%
1134560
8.2%
1035712
8.5%
934560
8.2%
835712
8.5%

day
Real number (ℝ≥0)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.72963723
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Memory size3.2 MiB
2021-01-09T20:38:29.296472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.800102498
Coefficient of variation (CV)0.5594599778
Kurtosis-1.194030263
Mean15.72963723
Median Absolute Deviation (MAD)8
Skewness0.006759540549
Sum6618528
Variance77.44180397
MonotocityNot monotonic
2021-01-09T20:38:29.393887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
113824
 
3.3%
213824
 
3.3%
2813824
 
3.3%
2713824
 
3.3%
2613824
 
3.3%
2513824
 
3.3%
2413824
 
3.3%
2313824
 
3.3%
2213824
 
3.3%
2113824
 
3.3%
Other values (21)282528
67.1%
ValueCountFrequency (%)
113824
3.3%
213824
3.3%
313824
3.3%
413824
3.3%
513824
3.3%
ValueCountFrequency (%)
318064
1.9%
3012672
3.0%
2912960
3.1%
2813824
3.3%
2713824
3.3%

hour
Real number (ℝ≥0)

ZEROS

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros17532
Zeros (%)4.2%
Memory size3.2 MiB
2021-01-09T20:38:29.473949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.922194778
Coefficient of variation (CV)0.6019299807
Kurtosis-1.204173963
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum4838832
Variance47.91678055
MonotocityNot monotonic
2021-01-09T20:38:29.550546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
017532
 
4.2%
117532
 
4.2%
2217532
 
4.2%
2117532
 
4.2%
2017532
 
4.2%
1917532
 
4.2%
1817532
 
4.2%
1717532
 
4.2%
1617532
 
4.2%
1517532
 
4.2%
Other values (14)245448
58.3%
ValueCountFrequency (%)
017532
4.2%
117532
4.2%
217532
4.2%
317532
4.2%
417532
4.2%
ValueCountFrequency (%)
2317532
4.2%
2217532
4.2%
2117532
4.2%
2017532
4.2%
1917532
4.2%

PM2.5
Real number (ℝ≥0)

Distinct888
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.27848933
Minimum2
Maximum999
Zeros0
Zeros (%)0.0%
Memory size3.2 MiB
2021-01-09T20:38:29.651570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q121
median55
Q3109
95-th percentile240
Maximum999
Range997
Interquartile range (IQR)88

Descriptive statistics

Standard deviation80.05679864
Coefficient of variation (CV)1.009817408
Kurtosis6.17192489
Mean79.27848933
Median Absolute Deviation (MAD)39
Skewness2.047948852
Sum33357851.4
Variance6409.091009
MonotocityNot monotonic
2021-01-09T20:38:29.771516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5511218
 
2.7%
38810
 
2.1%
107088
 
1.7%
96886
 
1.6%
86836
 
1.6%
116831
 
1.6%
126731
 
1.6%
76183
 
1.5%
136178
 
1.5%
146076
 
1.4%
Other values (878)347931
82.7%
ValueCountFrequency (%)
217
 
< 0.1%
38810
2.1%
43476
 
0.8%
4.32
 
< 0.1%
4.41
 
< 0.1%
ValueCountFrequency (%)
9991
< 0.1%
9571
< 0.1%
9411
< 0.1%
8981
< 0.1%
8821
< 0.1%

PM10
Real number (ℝ≥0)

Distinct1084
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.2561932
Minimum2
Maximum999
Zeros0
Zeros (%)0.0%
Memory size3.2 MiB
2021-01-09T20:38:29.870671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q136
median82
Q3144
95-th percentile278
Maximum999
Range997
Interquartile range (IQR)108

Descriptive statistics

Standard deviation91.10874488
Coefficient of variation (CV)0.8738928795
Kurtosis6.340266838
Mean104.2561932
Median Absolute Deviation (MAD)51
Skewness1.908980906
Sum43867669.9
Variance8300.803394
MonotocityNot monotonic
2021-01-09T20:38:29.973673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
828440
 
2.0%
64945
 
1.2%
183748
 
0.9%
53731
 
0.9%
143725
 
0.9%
163616
 
0.9%
173607
 
0.9%
133576
 
0.8%
203555
 
0.8%
153468
 
0.8%
Other values (1074)378357
89.9%
ValueCountFrequency (%)
2103
 
< 0.1%
3726
 
0.2%
4270
 
0.1%
53731
0.9%
5.42
 
< 0.1%
ValueCountFrequency (%)
9993
< 0.1%
9951
 
< 0.1%
9941
 
< 0.1%
9931
 
< 0.1%
9921
 
< 0.1%

SO2
Real number (ℝ≥0)

Distinct691
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.64150718
Minimum0.2856
Maximum500
Zeros0
Zeros (%)0.0%
Memory size3.2 MiB
2021-01-09T20:38:30.079188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.2856
5-th percentile2
Q13
median7
Q319
95-th percentile60
Maximum500
Range499.7144
Interquartile range (IQR)16

Descriptive statistics

Standard deviation21.45541881
Coefficient of variation (CV)1.37169766
Kurtosis14.54762913
Mean15.64150718
Median Absolute Deviation (MAD)5
Skewness3.051065785
Sum6581445.695
Variance460.3349961
MonotocityNot monotonic
2021-01-09T20:38:30.462621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
298557
23.4%
332784
 
7.8%
723410
 
5.6%
421716
 
5.2%
517964
 
4.3%
616567
 
3.9%
813348
 
3.2%
911559
 
2.7%
1010651
 
2.5%
119362
 
2.2%
Other values (681)164850
39.2%
ValueCountFrequency (%)
0.285689
 
< 0.1%
0.571270
 
< 0.1%
0.856872
 
< 0.1%
13258
0.8%
1.142484
 
< 0.1%
ValueCountFrequency (%)
5003
< 0.1%
4111
 
< 0.1%
3411
 
< 0.1%
3151
 
< 0.1%
3141
 
< 0.1%

NO2
Real number (ℝ≥0)

Distinct1213
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.63858559
Minimum1.0265
Maximum290
Zeros0
Zeros (%)0.0%
Memory size3.2 MiB
2021-01-09T20:38:30.580722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.0265
5-th percentile8
Q124
median45
Q370
95-th percentile117
Maximum290
Range288.9735
Interquartile range (IQR)46

Descriptive statistics

Standard deviation34.61846291
Coefficient of variation (CV)0.6836380302
Kurtosis1.328475761
Mean50.63858559
Median Absolute Deviation (MAD)23
Skewness1.065507904
Sum21307096.38
Variance1198.437974
MonotocityNot monotonic
2021-01-09T20:38:30.675761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.6385855912116
 
2.9%
165799
 
1.4%
225755
 
1.4%
205718
 
1.4%
175690
 
1.4%
185676
 
1.3%
215608
 
1.3%
265601
 
1.3%
145595
 
1.3%
195576
 
1.3%
Other values (1203)357634
85.0%
ValueCountFrequency (%)
1.02653
< 0.1%
1.23182
< 0.1%
1.43712
< 0.1%
1.64243
< 0.1%
1.84771
 
< 0.1%
ValueCountFrequency (%)
2901
< 0.1%
2851
< 0.1%
2801
< 0.1%
2772
< 0.1%
2761
< 0.1%

CO
Real number (ℝ≥0)

Distinct132
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1214.493362
Minimum100
Maximum10000
Zeros0
Zeros (%)0.0%
Memory size3.2 MiB
2021-01-09T20:38:30.778771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile200
Q1500
median900
Q31500
95-th percentile3400
Maximum10000
Range9900
Interquartile range (IQR)1000

Descriptive statistics

Standard deviation1133.542988
Coefficient of variation (CV)0.9333463839
Kurtosis10.00574467
Mean1214.493362
Median Absolute Deviation (MAD)400
Skewness2.661900608
Sum511019943
Variance1284919.705
MonotocityNot monotonic
2021-01-09T20:38:30.874651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90041931
 
10.0%
30032308
 
7.7%
40031275
 
7.4%
50029171
 
6.9%
60028035
 
6.7%
70026488
 
6.3%
80023370
 
5.6%
100019462
 
4.6%
20018434
 
4.4%
110017414
 
4.1%
Other values (122)152880
36.3%
ValueCountFrequency (%)
1005695
 
1.4%
1501
 
< 0.1%
20018434
4.4%
30032308
7.7%
3501
 
< 0.1%
ValueCountFrequency (%)
1000056
< 0.1%
990030
< 0.1%
980031
< 0.1%
970029
< 0.1%
960028
< 0.1%

O3
Real number (ℝ≥0)

Distinct1598
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.98187409
Minimum0.2142
Maximum1071
Zeros0
Zeros (%)0.0%
Memory size3.2 MiB
2021-01-09T20:38:30.971195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.2142
5-th percentile2
Q112
median45
Q380
95-th percentile175
Maximum1071
Range1070.7858
Interquartile range (IQR)68

Descriptive statistics

Standard deviation55.80241254
Coefficient of variation (CV)0.9793011099
Kurtosis6.584750256
Mean56.98187409
Median Absolute Deviation (MAD)34
Skewness1.703768067
Sum23976149.2
Variance3113.909245
MonotocityNot monotonic
2021-01-09T20:38:31.068207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
242327
 
10.1%
4516018
 
3.8%
38720
 
2.1%
48049
 
1.9%
16958
 
1.7%
56462
 
1.5%
66001
 
1.4%
84997
 
1.2%
74949
 
1.2%
94238
 
1.0%
Other values (1588)312049
74.2%
ValueCountFrequency (%)
0.2142134
 
< 0.1%
0.4284119
 
< 0.1%
0.6426118
 
< 0.1%
0.8568120
 
< 0.1%
16958
1.7%
ValueCountFrequency (%)
107114
< 0.1%
10501
 
< 0.1%
10261
 
< 0.1%
6741
 
< 0.1%
6731
 
< 0.1%

TEMP
Real number (ℝ)

Distinct2034
Distinct (%)0.5%
Missing398
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean13.53897583
Minimum-19.9
Maximum41.6
Zeros2739
Zeros (%)0.7%
Memory size3.2 MiB
2021-01-09T20:38:31.163278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-19.9
5-th percentile-4.2
Q13.1
median14.5
Q323.3
95-th percentile30.6
Maximum41.6
Range61.5
Interquartile range (IQR)20.2

Descriptive statistics

Standard deviation11.43613941
Coefficient of variation (CV)0.8446827551
Kurtosis-1.143301395
Mean13.53897583
Median Absolute Deviation (MAD)9.8
Skewness-0.1042674902
Sum5691379.271
Variance130.7852846
MonotocityNot monotonic
2021-01-09T20:38:31.260309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33414
 
0.8%
12903
 
0.7%
02739
 
0.7%
22687
 
0.6%
-12562
 
0.6%
-22396
 
0.6%
-41907
 
0.5%
41830
 
0.4%
51748
 
0.4%
-51662
 
0.4%
Other values (2024)396522
94.2%
ValueCountFrequency (%)
-19.91
< 0.1%
-19.71
< 0.1%
-19.51
< 0.1%
-18.91
< 0.1%
-18.71
< 0.1%
ValueCountFrequency (%)
41.61
 
< 0.1%
41.42
< 0.1%
41.13
< 0.1%
412
< 0.1%
40.91
 
< 0.1%

PRES
Real number (ℝ≥0)

Distinct726
Distinct (%)0.2%
Missing393
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1010.746982
Minimum982.4
Maximum1042.8
Zeros0
Zeros (%)0.0%
Memory size3.2 MiB
2021-01-09T20:38:31.361371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum982.4
5-th percentile994.7
Q11002.3
median1010.4
Q31019
95-th percentile1027.9
Maximum1042.8
Range60.4
Interquartile range (IQR)16.7

Descriptive statistics

Standard deviation10.47405475
Coefficient of variation (CV)0.01036268714
Kurtosis-0.8292032059
Mean1010.746982
Median Absolute Deviation (MAD)8.4
Skewness0.1063136066
Sum424892762.6
Variance109.7058229
MonotocityNot monotonic
2021-01-09T20:38:31.458042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10192948
 
0.7%
10212932
 
0.7%
10182868
 
0.7%
10202862
 
0.7%
10232838
 
0.7%
10222746
 
0.7%
10172707
 
0.6%
10162702
 
0.6%
10152700
 
0.6%
10242653
 
0.6%
Other values (716)392419
93.3%
ValueCountFrequency (%)
982.42
< 0.1%
982.72
< 0.1%
982.83
< 0.1%
982.92
< 0.1%
9834
< 0.1%
ValueCountFrequency (%)
1042.82
 
< 0.1%
1042.41
 
< 0.1%
1042.32
 
< 0.1%
1042.21
 
< 0.1%
104211
< 0.1%

DEWP
Real number (ℝ)

Distinct645
Distinct (%)0.2%
Missing403
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2.490822024
Minimum-43.4
Maximum29.1
Zeros832
Zeros (%)0.2%
Memory size3.2 MiB
2021-01-09T20:38:31.576058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-43.4
5-th percentile-19.9
Q1-8.9
median3.1
Q315.1
95-th percentile22.1
Maximum29.1
Range72.5
Interquartile range (IQR)24

Descriptive statistics

Standard deviation13.79384687
Coefficient of variation (CV)5.537869322
Kurtosis-1.132189242
Mean2.490822024
Median Absolute Deviation (MAD)12
Skewness-0.1877356196
Sum1047054.4
Variance190.2702115
MonotocityNot monotonic
2021-01-09T20:38:31.671832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.61559
 
0.4%
171519
 
0.4%
17.21490
 
0.4%
16.81483
 
0.4%
17.31455
 
0.3%
17.11445
 
0.3%
17.81440
 
0.3%
16.21429
 
0.3%
18.21426
 
0.3%
17.51409
 
0.3%
Other values (635)405710
96.4%
ValueCountFrequency (%)
-43.41
 
< 0.1%
-361
 
< 0.1%
-35.71
 
< 0.1%
-35.51
 
< 0.1%
-35.37
< 0.1%
ValueCountFrequency (%)
29.12
 
< 0.1%
291
 
< 0.1%
28.810
< 0.1%
28.712
< 0.1%
28.62
 
< 0.1%

RAIN
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct253
Distinct (%)0.1%
Missing390
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.06447578132
Minimum0
Maximum72.5
Zeros403858
Zeros (%)96.0%
Memory size3.2 MiB
2021-01-09T20:38:31.772218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum72.5
Range72.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8210041464
Coefficient of variation (CV)12.73352768
Kurtosis1345.506123
Mean0.06447578132
Median Absolute Deviation (MAD)0
Skewness30.04363268
Sum27104.2
Variance0.6740478083
MonotocityNot monotonic
2021-01-09T20:38:31.869014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0403858
96.0%
0.13722
 
0.9%
0.21841
 
0.4%
0.31382
 
0.3%
0.4907
 
0.2%
0.5860
 
0.2%
0.6702
 
0.2%
0.7599
 
0.1%
0.9504
 
0.1%
0.8488
 
0.1%
Other values (243)5515
 
1.3%
ValueCountFrequency (%)
0403858
96.0%
0.13722
 
0.9%
0.21841
 
0.4%
0.31382
 
0.3%
0.4907
 
0.2%
ValueCountFrequency (%)
72.53
< 0.1%
52.12
 
< 0.1%
47.71
 
< 0.1%
46.46
< 0.1%
45.92
 
< 0.1%

wd
Categorical

Distinct16
Distinct (%)< 0.1%
Missing1822
Missing (%)0.4%
Memory size3.2 MiB
NE
43335 
ENE
34142 
NW
32600 
N
30869 
E
29752 
Other values (11)
248248 

Length

Max length3
Median length2
Mean length2.236314465
Min length1

Characters and Unicode

Total characters936895
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNNW
2nd rowN
3rd rowNNW
4th rowNW
5th rowN
ValueCountFrequency (%)
NE43335
 
10.3%
ENE34142
 
8.1%
NW32600
 
7.7%
N30869
 
7.3%
E29752
 
7.1%
SW28756
 
6.8%
NNE28232
 
6.7%
NNW25326
 
6.0%
WNW24375
 
5.8%
ESE24220
 
5.8%
Other values (6)117339
27.9%
2021-01-09T20:38:32.060652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ne43335
 
10.3%
ene34142
 
8.1%
nw32600
 
7.8%
n30869
 
7.4%
e29752
 
7.1%
sw28756
 
6.9%
nne28232
 
6.7%
nnw25326
 
6.0%
wnw24375
 
5.8%
ese24220
 
5.8%
Other values (6)117339
28.0%

Most occurring characters

ValueCountFrequency (%)
N272437
29.1%
E255811
27.3%
W215638
23.0%
S193009
20.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter936895
100.0%

Most frequent character per category

ValueCountFrequency (%)
N272437
29.1%
E255811
27.3%
W215638
23.0%
S193009
20.6%

Most occurring scripts

ValueCountFrequency (%)
Latin936895
100.0%

Most frequent character per script

ValueCountFrequency (%)
N272437
29.1%
E255811
27.3%
W215638
23.0%
S193009
20.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII936895
100.0%

Most frequent character per block

ValueCountFrequency (%)
N272437
29.1%
E255811
27.3%
W215638
23.0%
S193009
20.6%

WSPM
Real number (ℝ≥0)

ZEROS

Distinct117
Distinct (%)< 0.1%
Missing318
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.729710548
Minimum0
Maximum13.2
Zeros11118
Zeros (%)2.6%
Memory size3.2 MiB
2021-01-09T20:38:32.149654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q10.9
median1.4
Q32.2
95-th percentile4.3
Maximum13.2
Range13.2
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.246385565
Coefficient of variation (CV)0.7205746457
Kurtosis3.684467043
Mean1.729710548
Median Absolute Deviation (MAD)0.6
Skewness1.625589782
Sum727256.8
Variance1.553476978
MonotocityNot monotonic
2021-01-09T20:38:32.253801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.122249
 
5.3%
122150
 
5.3%
1.222141
 
5.3%
0.920955
 
5.0%
1.320383
 
4.8%
0.819110
 
4.5%
1.418523
 
4.4%
0.717543
 
4.2%
1.517019
 
4.0%
1.615793
 
3.8%
Other values (107)224584
53.4%
ValueCountFrequency (%)
011118
2.6%
0.14288
 
1.0%
0.24529
1.1%
0.32841
 
0.7%
0.47328
1.7%
ValueCountFrequency (%)
13.21
< 0.1%
12.91
< 0.1%
12.81
< 0.1%
121
< 0.1%
11.81
< 0.1%

station
Categorical

UNIFORM

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
Aotizhongxin
35064 
Shunyi
35064 
Gucheng
35064 
Wanshouxigong
35064 
Wanliu
35064 
Other values (7)
245448 

Length

Max length13
Median length7.5
Mean length8.416666667
Min length6

Characters and Unicode

Total characters3541464
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAotizhongxin
2nd rowAotizhongxin
3rd rowAotizhongxin
4th rowAotizhongxin
5th rowAotizhongxin
ValueCountFrequency (%)
Aotizhongxin35064
8.3%
Shunyi35064
8.3%
Gucheng35064
8.3%
Wanshouxigong35064
8.3%
Wanliu35064
8.3%
Dongsi35064
8.3%
Changping35064
8.3%
Huairou35064
8.3%
Dingling35064
8.3%
Nongzhanguan35064
8.3%
Other values (2)70128
16.7%
2021-01-09T20:38:32.433297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dongsi35064
8.3%
gucheng35064
8.3%
aotizhongxin35064
8.3%
changping35064
8.3%
tiantan35064
8.3%
huairou35064
8.3%
shunyi35064
8.3%
guanyuan35064
8.3%
wanliu35064
8.3%
dingling35064
8.3%
Other values (2)70128
16.7%

Most occurring characters

ValueCountFrequency (%)
n666216
18.8%
i385704
10.9%
g385704
10.9%
a350640
9.9%
u315576
8.9%
o245448
 
6.9%
h210384
 
5.9%
t70128
 
2.0%
z70128
 
2.0%
x70128
 
2.0%
Other values (16)771408
21.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3120696
88.1%
Uppercase Letter420768
 
11.9%

Most frequent character per category

ValueCountFrequency (%)
n666216
21.3%
i385704
12.4%
g385704
12.4%
a350640
11.2%
u315576
10.1%
o245448
 
7.9%
h210384
 
6.7%
t70128
 
2.2%
z70128
 
2.2%
x70128
 
2.2%
Other values (7)350640
11.2%
ValueCountFrequency (%)
D70128
16.7%
G70128
16.7%
W70128
16.7%
A35064
8.3%
C35064
8.3%
H35064
8.3%
N35064
8.3%
S35064
8.3%
T35064
8.3%

Most occurring scripts

ValueCountFrequency (%)
Latin3541464
100.0%

Most frequent character per script

ValueCountFrequency (%)
n666216
18.8%
i385704
10.9%
g385704
10.9%
a350640
9.9%
u315576
8.9%
o245448
 
6.9%
h210384
 
5.9%
t70128
 
2.0%
z70128
 
2.0%
x70128
 
2.0%
Other values (16)771408
21.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII3541464
100.0%

Most frequent character per block

ValueCountFrequency (%)
n666216
18.8%
i385704
10.9%
g385704
10.9%
a350640
9.9%
u315576
8.9%
o245448
 
6.9%
h210384
 
5.9%
t70128
 
2.0%
z70128
 
2.0%
x70128
 
2.0%
Other values (16)771408
21.8%

Interactions

2021-01-09T20:37:27.863858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:28.105900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:28.337914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:28.591792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:28.841790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:29.099820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:29.342499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:29.587309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:29.858361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:30.104765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:30.340296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:30.575212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:30.816867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:31.030456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:31.271447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:31.502517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:31.730513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:31.957426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:32.186586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:32.420662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:32.666537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:32.921639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:33.249772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:33.485693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:33.721692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:33.965730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:34.216782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:34.449208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:34.655994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:34.895025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:35.126544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:35.351610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:35.575382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:35.798982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:36.020432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:36.254224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:36.482260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:36.714260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:36.943289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:37.168291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:37.424512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:37.649992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:37.883577image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:38.091665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:38.333782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:38.572013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:38.807737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:39.044820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:39.274390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:39.515894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:39.747866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:39.982430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:40.259159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:40.493504image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:40.728719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:41.072369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:41.302935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:41.538276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:41.744835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:41.986605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:42.211709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:42.450035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:42.684994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:42.901558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:43.134249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:43.373971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:43.610017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:43.837059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:44.082922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-01-09T20:37:44.537905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:44.763429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:44.996692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:45.201251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-01-09T20:37:45.921300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:46.153735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-01-09T20:37:46.624681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:46.855619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:47.083411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-01-09T20:37:47.532263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:47.754298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:47.991823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:48.230857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:48.475986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:48.680477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:48.910309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:49.144347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:49.377603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:49.614978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:49.840020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:50.080453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:50.302636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:50.670686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:50.908662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:51.135830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:51.399703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:51.657209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:51.894370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:52.128251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:52.362395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:52.616036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:52.877931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:53.126147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:53.371098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:53.612813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:53.845992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:54.106967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:54.361049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:54.596052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:54.837572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-01-09T20:37:55.311955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:55.559675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:55.792678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:56.008213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:56.260735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:56.497388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:56.736399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:56.969670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:57.209665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:57.451786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:57.676868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:57.908904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-01-09T20:37:58.853816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-01-09T20:37:59.762828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:37:59.992965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-01-09T20:38:00.451608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:38:00.679047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:38:00.924532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:38:01.148661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-01-09T20:38:01.827907image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:38:02.071853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-01-09T20:38:07.333980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-01-09T20:38:09.444579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:38:09.678289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-01-09T20:38:10.364824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-01-09T20:38:11.560965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:38:11.788040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-01-09T20:38:12.519571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:38:12.765179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:38:13.001644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:38:13.252265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:38:13.513338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:38:13.748060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:38:14.009165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:38:14.255713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-01-09T20:38:14.754331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:38:15.014401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-01-09T20:38:16.216858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-01-09T20:38:22.787789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-01-09T20:38:23.966880image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-01-09T20:38:24.653618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:38:24.900679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-09T20:38:25.116945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-01-09T20:38:32.523848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-01-09T20:38:32.675394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-01-09T20:38:32.838392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-01-09T20:38:32.996749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-01-09T20:38:33.121763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-01-09T20:38:25.722068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-01-09T20:38:26.467753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-01-09T20:38:27.711799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-01-09T20:38:27.988812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexNoyearmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINwdWSPMstation
00120133104.04.04.07.0300.077.0-0.71023.0-18.80.0NNW4.4Aotizhongxin
11220133118.08.04.07.0300.077.0-1.11023.2-18.20.0N4.7Aotizhongxin
22320133127.07.05.010.0300.073.0-1.11023.5-18.20.0NNW5.6Aotizhongxin
33420133136.06.011.011.0300.072.0-1.41024.5-19.40.0NW3.1Aotizhongxin
44520133143.03.012.012.0300.072.0-2.01025.2-19.50.0N2.0Aotizhongxin
55620133155.05.018.018.0400.066.0-2.21025.6-19.60.0N3.7Aotizhongxin
66720133163.03.018.032.0500.050.0-2.61026.5-19.10.0NNE2.5Aotizhongxin
77820133173.06.019.041.0500.043.0-1.61027.4-19.10.0NNW3.8Aotizhongxin
88920133183.06.016.043.0500.045.00.11028.3-19.20.0NNW4.1Aotizhongxin
991020133193.08.012.028.0400.059.01.21028.5-19.30.0N2.6Aotizhongxin

Last rows

df_indexNoyearmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINwdWSPMstation
42075835054350552017228143.06.07.05.000000900.082.014.61013.3-15.60.0N3.6Wanshouxigong
420759350553505620172281511.021.02.05.000000200.045.015.41013.0-15.00.0NNW3.3Wanshouxigong
42076035056350572017228166.020.03.050.638586200.080.014.91012.6-15.40.0NW2.1Wanshouxigong
420761350573505820172281711.023.03.012.000000300.087.014.21012.5-14.90.0NW3.1Wanshouxigong
420762350583505920172281811.030.02.016.000000300.082.013.41013.0-15.50.0WNW1.4Wanshouxigong
420763350593506020172281911.032.03.024.000000400.072.012.51013.5-16.20.0NW2.4Wanshouxigong
420764350603506120172282013.032.03.041.000000500.050.011.61013.6-15.10.0WNW0.9Wanshouxigong
420765350613506220172282114.028.04.038.000000500.054.010.81014.2-13.30.0NW1.1Wanshouxigong
420766350623506320172282212.023.04.030.000000400.059.010.51014.4-12.90.0NNW1.2Wanshouxigong
420767350633506420172282313.019.04.038.000000600.049.08.61014.1-15.90.0NNE1.3Wanshouxigong